Overview

Dataset statistics

Number of variables26
Number of observations124
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.3 KiB
Average record size in memory209.0 B

Variable types

Numeric17
Categorical9

Alerts

base_health has constant value "120"Constant
base_mr has constant value "25"Constant
name has a high cardinality: 124 distinct valuesHigh cardinality
base_armor is highly overall correlated with base_manaHigh correlation
base_str is highly overall correlated with str_gainHigh correlation
base_agi is highly overall correlated with agi_gainHigh correlation
base_int is highly overall correlated with int_gain and 1 other fieldsHigh correlation
str_gain is highly overall correlated with base_str and 1 other fieldsHigh correlation
agi_gain is highly overall correlated with base_agiHigh correlation
int_gain is highly overall correlated with base_int and 3 other fieldsHigh correlation
attack_range is highly overall correlated with str_gain and 3 other fieldsHigh correlation
projectile_speed is highly overall correlated with attack_range and 1 other fieldsHigh correlation
base_attack is highly overall correlated with primary_attr and 1 other fieldsHigh correlation
primary_attr is highly overall correlated with base_attack and 1 other fieldsHigh correlation
attack_type is highly overall correlated with int_gain and 3 other fieldsHigh correlation
base_mana is highly overall correlated with base_armor and 3 other fieldsHigh correlation
base_mana is highly imbalanced (93.2%)Imbalance
legs is highly imbalanced (54.1%)Imbalance
day_vision is highly imbalanced (91.5%)Imbalance
night_vision is highly imbalanced (86.2%)Imbalance
name is uniformly distributedUniform
primary_attr is uniformly distributedUniform
hero_id has unique valuesUnique
name has unique valuesUnique
base_mana_regen has 93 (75.0%) zerosZeros
base_armor has 40 (32.3%) zerosZeros
projectile_speed has 10 (8.1%) zerosZeros

Reproduction

Analysis started2023-08-13 16:31:54.008555
Analysis finished2023-08-13 16:32:58.293587
Duration1 minute and 4.29 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

hero_id
Real number (ℝ)

Distinct124
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.056452
Minimum1
Maximum138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-13T19:32:58.506146image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.15
Q132.75
median63.5
Q394.25
95-th percentile125.55
Maximum138
Range137
Interquartile range (IQR)61.5

Descriptive statistics

Standard deviation37.476779
Coefficient of variation (CV)0.58505862
Kurtosis-1.035741
Mean64.056452
Median Absolute Deviation (MAD)31
Skewness0.098773275
Sum7943
Variance1404.509
MonotonicityStrictly increasing
2023-08-13T19:32:58.782146image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
80 1
 
0.8%
93 1
 
0.8%
92 1
 
0.8%
91 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
Other values (114) 114
91.9%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
138 1
0.8%
137 1
0.8%
136 1
0.8%
135 1
0.8%
129 1
0.8%
128 1
0.8%
126 1
0.8%
123 1
0.8%
121 1
0.8%
120 1
0.8%

name
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct124
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Anti-Mage
 
1
Lone Druid
 
1
Slark
 
1
Visage
 
1
Io
 
1
Other values (119)
119 

Length

Max length19
Median length15
Mean length9.0806452
Min length2

Characters and Unicode

Total characters1126
Distinct characters51
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)100.0%

Sample

1st rowAnti-Mage
2nd rowAxe
3rd rowBane
4th rowBloodseeker
5th rowCrystal Maiden

Common Values

ValueCountFrequency (%)
Anti-Mage 1
 
0.8%
Lone Druid 1
 
0.8%
Slark 1
 
0.8%
Visage 1
 
0.8%
Io 1
 
0.8%
Keeper of the Light 1
 
0.8%
Naga Siren 1
 
0.8%
Nyx Assassin 1
 
0.8%
Disruptor 1
 
0.8%
Rubick 1
 
0.8%
Other values (114) 114
91.9%

Length

2023-08-13T19:32:59.085146image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
spirit 6
 
3.5%
king 3
 
1.8%
shadow 3
 
1.8%
assassin 3
 
1.8%
dark 2
 
1.2%
void 2
 
1.2%
knight 2
 
1.2%
phantom 2
 
1.2%
of 2
 
1.2%
prophet 2
 
1.2%
Other values (144) 144
84.2%

Most occurring characters

ValueCountFrequency (%)
r 111
 
9.9%
e 108
 
9.6%
a 90
 
8.0%
n 78
 
6.9%
i 76
 
6.7%
o 61
 
5.4%
t 57
 
5.1%
47
 
4.2%
s 43
 
3.8%
h 33
 
2.9%
Other values (41) 422
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 908
80.6%
Uppercase Letter 169
 
15.0%
Space Separator 47
 
4.2%
Dash Punctuation 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 111
12.2%
e 108
11.9%
a 90
9.9%
n 78
 
8.6%
i 76
 
8.4%
o 61
 
6.7%
t 57
 
6.3%
s 43
 
4.7%
h 33
 
3.6%
d 29
 
3.2%
Other values (14) 222
24.4%
Uppercase Letter
ValueCountFrequency (%)
S 23
13.6%
D 13
 
7.7%
M 13
 
7.7%
P 12
 
7.1%
L 11
 
6.5%
W 11
 
6.5%
T 11
 
6.5%
B 10
 
5.9%
A 10
 
5.9%
C 7
 
4.1%
Other values (14) 48
28.4%
Space Separator
ValueCountFrequency (%)
47
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1077
95.6%
Common 49
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 111
 
10.3%
e 108
 
10.0%
a 90
 
8.4%
n 78
 
7.2%
i 76
 
7.1%
o 61
 
5.7%
t 57
 
5.3%
s 43
 
4.0%
h 33
 
3.1%
d 29
 
2.7%
Other values (38) 391
36.3%
Common
ValueCountFrequency (%)
47
95.9%
- 1
 
2.0%
' 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 111
 
9.9%
e 108
 
9.6%
a 90
 
8.0%
n 78
 
6.9%
i 76
 
6.7%
o 61
 
5.4%
t 57
 
5.1%
47
 
4.2%
s 43
 
3.8%
h 33
 
2.9%
Other values (41) 422
37.5%

primary_attr
Categorical

HIGH CORRELATION  UNIFORM 

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
agi
31 
str
31 
all
31 
int
31 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters372
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowagi
2nd rowstr
3rd rowall
4th rowagi
5th rowint

Common Values

ValueCountFrequency (%)
agi 31
25.0%
str 31
25.0%
all 31
25.0%
int 31
25.0%

Length

2023-08-13T19:32:59.328148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T19:32:59.586117image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
agi 31
25.0%
str 31
25.0%
all 31
25.0%
int 31
25.0%

Most occurring characters

ValueCountFrequency (%)
a 62
16.7%
i 62
16.7%
t 62
16.7%
l 62
16.7%
g 31
8.3%
s 31
8.3%
r 31
8.3%
n 31
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 372
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 62
16.7%
i 62
16.7%
t 62
16.7%
l 62
16.7%
g 31
8.3%
s 31
8.3%
r 31
8.3%
n 31
8.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 372
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 62
16.7%
i 62
16.7%
t 62
16.7%
l 62
16.7%
g 31
8.3%
s 31
8.3%
r 31
8.3%
n 31
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 372
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 62
16.7%
i 62
16.7%
t 62
16.7%
l 62
16.7%
g 31
8.3%
s 31
8.3%
r 31
8.3%
n 31
8.3%

attack_type
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Ranged
64 
Melee
60 

Length

Max length6
Median length6
Mean length5.516129
Min length5

Characters and Unicode

Total characters684
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMelee
2nd rowMelee
3rd rowRanged
4th rowMelee
5th rowRanged

Common Values

ValueCountFrequency (%)
Ranged 64
51.6%
Melee 60
48.4%

Length

2023-08-13T19:32:59.811119image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T19:33:00.047117image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
ranged 64
51.6%
melee 60
48.4%

Most occurring characters

ValueCountFrequency (%)
e 244
35.7%
R 64
 
9.4%
a 64
 
9.4%
n 64
 
9.4%
g 64
 
9.4%
d 64
 
9.4%
M 60
 
8.8%
l 60
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 560
81.9%
Uppercase Letter 124
 
18.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 244
43.6%
a 64
 
11.4%
n 64
 
11.4%
g 64
 
11.4%
d 64
 
11.4%
l 60
 
10.7%
Uppercase Letter
ValueCountFrequency (%)
R 64
51.6%
M 60
48.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 684
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 244
35.7%
R 64
 
9.4%
a 64
 
9.4%
n 64
 
9.4%
g 64
 
9.4%
d 64
 
9.4%
M 60
 
8.8%
l 60
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 244
35.7%
R 64
 
9.4%
a 64
 
9.4%
n 64
 
9.4%
g 64
 
9.4%
d 64
 
9.4%
M 60
 
8.8%
l 60
 
8.8%

base_health
Categorical

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
120
124 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters372
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row120
2nd row120
3rd row120
4th row120
5th row120

Common Values

ValueCountFrequency (%)
120 124
100.0%

Length

2023-08-13T19:33:00.243116image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T19:33:00.461119image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
120 124
100.0%

Most occurring characters

ValueCountFrequency (%)
1 124
33.3%
2 124
33.3%
0 124
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 372
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 124
33.3%
2 124
33.3%
0 124
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 372
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 124
33.3%
2 124
33.3%
0 124
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 372
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 124
33.3%
2 124
33.3%
0 124
33.3%

base_health_regen
Real number (ℝ)

Distinct10
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61693548
Minimum0.25
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-13T19:33:00.613117image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile0.25
Q10.25
median0.25
Q30.75
95-th percentile2
Maximum5
Range4.75
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.67130158
Coefficient of variation (CV)1.0881228
Kurtosis14.626395
Mean0.61693548
Median Absolute Deviation (MAD)0
Skewness3.1347689
Sum76.5
Variance0.45064582
MonotonicityNot monotonic
2023-08-13T19:33:00.807116image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.25 75
60.5%
1 12
 
9.7%
0.5 12
 
9.7%
0.75 7
 
5.6%
2 5
 
4.0%
1.25 5
 
4.0%
1.75 3
 
2.4%
2.5 2
 
1.6%
1.5 2
 
1.6%
5 1
 
0.8%
ValueCountFrequency (%)
0.25 75
60.5%
0.5 12
 
9.7%
0.75 7
 
5.6%
1 12
 
9.7%
1.25 5
 
4.0%
1.5 2
 
1.6%
1.75 3
 
2.4%
2 5
 
4.0%
2.5 2
 
1.6%
5 1
 
0.8%
ValueCountFrequency (%)
5 1
 
0.8%
2.5 2
 
1.6%
2 5
 
4.0%
1.75 3
 
2.4%
1.5 2
 
1.6%
1.25 5
 
4.0%
1 12
 
9.7%
0.75 7
 
5.6%
0.5 12
 
9.7%
0.25 75
60.5%

base_mana
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
75
123 
120
 
1

Length

Max length3
Median length2
Mean length2.0080645
Min length2

Characters and Unicode

Total characters249
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row75
2nd row75
3rd row75
4th row75
5th row75

Common Values

ValueCountFrequency (%)
75 123
99.2%
120 1
 
0.8%

Length

2023-08-13T19:33:01.024116image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T19:33:01.259272image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
75 123
99.2%
120 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
7 123
49.4%
5 123
49.4%
1 1
 
0.4%
2 1
 
0.4%
0 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 249
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 123
49.4%
5 123
49.4%
1 1
 
0.4%
2 1
 
0.4%
0 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 249
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 123
49.4%
5 123
49.4%
1 1
 
0.4%
2 1
 
0.4%
0 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 249
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 123
49.4%
5 123
49.4%
1 1
 
0.4%
2 1
 
0.4%
0 1
 
0.4%

base_mana_regen
Real number (ℝ)

Distinct8
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12822581
Minimum0
Maximum1
Zeros93
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-13T19:33:01.431272image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.0625
95-th percentile0.585
Maximum1
Range1
Interquartile range (IQR)0.0625

Descriptive statistics

Standard deviation0.24162744
Coefficient of variation (CV)1.8843901
Kurtosis2.0842966
Mean0.12822581
Median Absolute Deviation (MAD)0
Skewness1.7384558
Sum15.9
Variance0.058383819
MonotonicityNot monotonic
2023-08-13T19:33:01.632271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 93
75.0%
0.5 17
 
13.7%
0.25 5
 
4.0%
0.75 3
 
2.4%
1 2
 
1.6%
0.6 2
 
1.6%
0.4 1
 
0.8%
0.3 1
 
0.8%
ValueCountFrequency (%)
0 93
75.0%
0.25 5
 
4.0%
0.3 1
 
0.8%
0.4 1
 
0.8%
0.5 17
 
13.7%
0.6 2
 
1.6%
0.75 3
 
2.4%
1 2
 
1.6%
ValueCountFrequency (%)
1 2
 
1.6%
0.75 3
 
2.4%
0.6 2
 
1.6%
0.5 17
 
13.7%
0.4 1
 
0.8%
0.3 1
 
0.8%
0.25 5
 
4.0%
0 93
75.0%

base_armor
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47983871
Minimum-3
Maximum5
Zeros40
Zeros (%)32.3%
Negative30
Negative (%)24.2%
Memory size1.1 KiB
2023-08-13T19:33:01.831272image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile-1.85
Q10
median0
Q31
95-th percentile3
Maximum5
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4502582
Coefficient of variation (CV)3.0223868
Kurtosis0.67753099
Mean0.47983871
Median Absolute Deviation (MAD)1
Skewness0.58115903
Sum59.5
Variance2.1032488
MonotonicityNot monotonic
2023-08-13T19:33:02.029272image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 40
32.3%
1 26
21.0%
-1 23
18.5%
2 18
14.5%
-2 6
 
4.8%
3 5
 
4.0%
5 2
 
1.6%
4 2
 
1.6%
-3 1
 
0.8%
2.5 1
 
0.8%
ValueCountFrequency (%)
-3 1
 
0.8%
-2 6
 
4.8%
-1 23
18.5%
0 40
32.3%
1 26
21.0%
2 18
14.5%
2.5 1
 
0.8%
3 5
 
4.0%
4 2
 
1.6%
5 2
 
1.6%
ValueCountFrequency (%)
5 2
 
1.6%
4 2
 
1.6%
3 5
 
4.0%
2.5 1
 
0.8%
2 18
14.5%
1 26
21.0%
0 40
32.3%
-1 23
18.5%
-2 6
 
4.8%
-3 1
 
0.8%

base_mr
Categorical

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
25
124 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters248
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25
2nd row25
3rd row25
4th row25
5th row25

Common Values

ValueCountFrequency (%)
25 124
100.0%

Length

2023-08-13T19:33:02.785338image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T19:33:03.005338image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
25 124
100.0%

Most occurring characters

ValueCountFrequency (%)
2 124
50.0%
5 124
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 248
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 124
50.0%
5 124
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 248
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 124
50.0%
5 124
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 248
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 124
50.0%
5 124
50.0%

base_str
Real number (ℝ)

Distinct15
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.209677
Minimum0
Maximum30
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-13T19:33:03.175338image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q119
median21
Q323
95-th percentile26
Maximum30
Range30
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.4927898
Coefficient of variation (CV)0.16467906
Kurtosis9.9539125
Mean21.209677
Median Absolute Deviation (MAD)2
Skewness-1.548906
Sum2630
Variance12.19958
MonotonicityNot monotonic
2023-08-13T19:33:03.380338image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
22 17
13.7%
23 16
12.9%
21 15
12.1%
19 13
10.5%
20 13
10.5%
18 12
9.7%
25 11
8.9%
17 8
6.5%
24 5
 
4.0%
26 4
 
3.2%
Other values (5) 10
8.1%
ValueCountFrequency (%)
0 1
 
0.8%
16 3
 
2.4%
17 8
6.5%
18 12
9.7%
19 13
10.5%
20 13
10.5%
21 15
12.1%
22 17
13.7%
23 16
12.9%
24 5
 
4.0%
ValueCountFrequency (%)
30 1
 
0.8%
28 2
 
1.6%
27 3
 
2.4%
26 4
 
3.2%
25 11
8.9%
24 5
 
4.0%
23 16
12.9%
22 17
13.7%
21 15
12.1%
20 13
10.5%

base_agi
Real number (ℝ)

Distinct20
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.306452
Minimum0
Maximum34
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-13T19:33:03.607337image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q115
median18
Q322
95-th percentile24
Maximum34
Range34
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.4578923
Coefficient of variation (CV)0.24351482
Kurtosis2.0287764
Mean18.306452
Median Absolute Deviation (MAD)3
Skewness-0.23382309
Sum2270
Variance19.872804
MonotonicityNot monotonic
2023-08-13T19:33:03.820338image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
15 14
11.3%
22 13
10.5%
18 12
9.7%
20 11
8.9%
23 10
8.1%
16 10
8.1%
17 9
 
7.3%
14 8
 
6.5%
24 7
 
5.6%
19 7
 
5.6%
Other values (10) 23
18.5%
ValueCountFrequency (%)
0 1
 
0.8%
10 3
 
2.4%
11 2
 
1.6%
12 4
 
3.2%
13 3
 
2.4%
14 8
6.5%
15 14
11.3%
16 10
8.1%
17 9
7.3%
18 12
9.7%
ValueCountFrequency (%)
34 1
 
0.8%
27 1
 
0.8%
26 2
 
1.6%
25 1
 
0.8%
24 7
5.6%
23 10
8.1%
22 13
10.5%
21 5
 
4.0%
20 11
8.9%
19 7
5.6%

base_int
Real number (ℝ)

Distinct18
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.798387
Minimum0
Maximum30
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-13T19:33:04.050792image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q117
median20
Q323
95-th percentile25.85
Maximum30
Range30
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2426021
Coefficient of variation (CV)0.21429029
Kurtosis2.8511452
Mean19.798387
Median Absolute Deviation (MAD)3
Skewness-0.54460014
Sum2455
Variance17.999672
MonotonicityNot monotonic
2023-08-13T19:33:04.256791image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
18 16
12.9%
21 11
8.9%
23 11
8.9%
16 11
8.9%
15 11
8.9%
22 10
8.1%
25 10
8.1%
20 10
8.1%
19 9
7.3%
24 6
 
4.8%
Other values (8) 19
15.3%
ValueCountFrequency (%)
0 1
 
0.8%
12 1
 
0.8%
13 2
 
1.6%
14 4
 
3.2%
15 11
8.9%
16 11
8.9%
17 4
 
3.2%
18 16
12.9%
19 9
7.3%
20 10
8.1%
ValueCountFrequency (%)
30 3
 
2.4%
27 1
 
0.8%
26 3
 
2.4%
25 10
8.1%
24 6
4.8%
23 11
8.9%
22 10
8.1%
21 11
8.9%
20 10
8.1%
19 9
7.3%

str_gain
Real number (ℝ)

Distinct27
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6145161
Minimum0
Maximum4.3
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-13T19:33:04.492835image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.915
Q12.2
median2.5
Q33
95-th percentile3.785
Maximum4.3
Range4.3
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.62815947
Coefficient of variation (CV)0.2402584
Kurtosis1.9545302
Mean2.6145161
Median Absolute Deviation (MAD)0.4
Skewness0.12035388
Sum324.2
Variance0.39458432
MonotonicityNot monotonic
2023-08-13T19:33:04.720835image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2 19
15.3%
2.4 14
11.3%
2.5 13
10.5%
3 11
 
8.9%
2.6 9
 
7.3%
2.2 8
 
6.5%
2.7 5
 
4.0%
2.8 5
 
4.0%
2.3 4
 
3.2%
3.7 4
 
3.2%
Other values (17) 32
25.8%
ValueCountFrequency (%)
0 1
 
0.8%
1.6 1
 
0.8%
1.7 2
 
1.6%
1.9 3
 
2.4%
2 19
15.3%
2.1 2
 
1.6%
2.2 8
6.5%
2.3 4
 
3.2%
2.4 14
11.3%
2.5 13
10.5%
ValueCountFrequency (%)
4.3 1
 
0.8%
4.2 1
 
0.8%
4 2
1.6%
3.9 1
 
0.8%
3.8 2
1.6%
3.7 4
3.2%
3.6 2
1.6%
3.5 1
 
0.8%
3.4 2
1.6%
3.3 3
2.4%

agi_gain
Real number (ℝ)

Distinct30
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0685484
Minimum0
Maximum4
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-13T19:33:04.958835image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.5
median2
Q32.525
95-th percentile3.4
Maximum4
Range4
Interquartile range (IQR)1.025

Descriptive statistics

Standard deviation0.76033443
Coefficient of variation (CV)0.36756908
Kurtosis-0.27117996
Mean2.0685484
Median Absolute Deviation (MAD)0.5
Skewness0.42810211
Sum256.5
Variance0.57810844
MonotonicityNot monotonic
2023-08-13T19:33:05.214835image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1.4 12
 
9.7%
2 10
 
8.1%
1.5 9
 
7.3%
2.5 7
 
5.6%
1.6 7
 
5.6%
1.8 6
 
4.8%
1 6
 
4.8%
1.7 6
 
4.8%
2.2 6
 
4.8%
2.4 5
 
4.0%
Other values (20) 50
40.3%
ValueCountFrequency (%)
0 1
 
0.8%
0.8 1
 
0.8%
1 6
4.8%
1.2 4
 
3.2%
1.3 5
4.0%
1.4 12
9.7%
1.5 9
7.3%
1.6 7
5.6%
1.7 6
4.8%
1.8 6
4.8%
ValueCountFrequency (%)
4 1
 
0.8%
3.9 1
 
0.8%
3.7 1
 
0.8%
3.6 1
 
0.8%
3.5 1
 
0.8%
3.4 3
2.4%
3.3 3
2.4%
3.2 5
4.0%
3.1 2
 
1.6%
3 3
2.4%

int_gain
Real number (ℝ)

Distinct34
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3370968
Minimum0
Maximum5.2
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-13T19:33:05.484875image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.2
Q11.7
median2.1
Q33
95-th percentile3.8
Maximum5.2
Range5.2
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation0.896195
Coefficient of variation (CV)0.38346508
Kurtosis0.0057961828
Mean2.3370968
Median Absolute Deviation (MAD)0.6
Skewness0.58805417
Sum289.8
Variance0.80316549
MonotonicityNot monotonic
2023-08-13T19:33:05.747875image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1.8 11
 
8.9%
2 8
 
6.5%
2.2 8
 
6.5%
1.6 8
 
6.5%
1.7 7
 
5.6%
1.4 6
 
4.8%
1.2 6
 
4.8%
3.8 6
 
4.8%
3.3 5
 
4.0%
1.5 5
 
4.0%
Other values (24) 54
43.5%
ValueCountFrequency (%)
0 1
 
0.8%
1 1
 
0.8%
1.2 6
4.8%
1.3 3
 
2.4%
1.4 6
4.8%
1.5 5
4.0%
1.6 8
6.5%
1.7 7
5.6%
1.8 11
8.9%
1.9 4
 
3.2%
ValueCountFrequency (%)
5.2 1
 
0.8%
4.6 1
 
0.8%
4.2 1
 
0.8%
4.1 1
 
0.8%
3.9 1
 
0.8%
3.8 6
4.8%
3.7 1
 
0.8%
3.6 2
 
1.6%
3.5 4
3.2%
3.4 2
 
1.6%

attack_range
Real number (ℝ)

Distinct27
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350.04032
Minimum150
Maximum700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-13T19:33:05.995348image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile150
Q1150
median340
Q3550
95-th percentile625
Maximum700
Range550
Interquartile range (IQR)400

Descriptive statistics

Standard deviation200.08687
Coefficient of variation (CV)0.57161091
Kurtosis-1.7071953
Mean350.04032
Median Absolute Deviation (MAD)190
Skewness0.21328848
Sum43405
Variance40034.754
MonotonicityNot monotonic
2023-08-13T19:33:06.213346image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
150 53
42.7%
600 13
 
10.5%
500 8
 
6.5%
550 7
 
5.6%
575 6
 
4.8%
400 6
 
4.8%
625 4
 
3.2%
200 4
 
3.2%
475 2
 
1.6%
450 2
 
1.6%
Other values (17) 19
 
15.3%
ValueCountFrequency (%)
150 53
42.7%
175 1
 
0.8%
200 4
 
3.2%
225 1
 
0.8%
250 1
 
0.8%
300 1
 
0.8%
330 1
 
0.8%
350 1
 
0.8%
365 1
 
0.8%
380 1
 
0.8%
ValueCountFrequency (%)
700 1
 
0.8%
675 1
 
0.8%
670 1
 
0.8%
650 1
 
0.8%
630 2
 
1.6%
625 4
 
3.2%
620 1
 
0.8%
600 13
10.5%
575 6
4.8%
550 7
5.6%

projectile_speed
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean970.16129
Minimum0
Maximum3000
Zeros10
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-13T19:33:06.436347image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1900
median900
Q31100
95-th percentile1500
Maximum3000
Range3000
Interquartile range (IQR)200

Descriptive statistics

Standard deviation465.54756
Coefficient of variation (CV)0.47986614
Kurtosis8.2213383
Mean970.16129
Median Absolute Deviation (MAD)0
Skewness1.5443546
Sum120300
Variance216734.53
MonotonicityNot monotonic
2023-08-13T19:33:06.632347image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
900 78
62.9%
1200 11
 
8.9%
0 10
 
8.1%
1100 5
 
4.0%
1250 3
 
2.4%
1000 3
 
2.4%
3000 3
 
2.4%
1500 2
 
1.6%
1125 2
 
1.6%
1400 2
 
1.6%
Other values (4) 5
 
4.0%
ValueCountFrequency (%)
0 10
 
8.1%
700 1
 
0.8%
900 78
62.9%
1000 3
 
2.4%
1100 5
 
4.0%
1125 2
 
1.6%
1200 11
 
8.9%
1250 3
 
2.4%
1300 1
 
0.8%
1400 2
 
1.6%
ValueCountFrequency (%)
3000 3
 
2.4%
2000 1
 
0.8%
1800 2
 
1.6%
1500 2
 
1.6%
1400 2
 
1.6%
1300 1
 
0.8%
1250 3
 
2.4%
1200 11
8.9%
1125 2
 
1.6%
1100 5
4.0%

attack_rate
Real number (ℝ)

Distinct7
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6919355
Minimum1.4
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-13T19:33:06.842347image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile1.5
Q11.7
median1.7
Q31.7
95-th percentile1.8
Maximum2
Range0.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.084195643
Coefficient of variation (CV)0.049762916
Kurtosis4.3416984
Mean1.6919355
Median Absolute Deviation (MAD)0
Skewness-0.34390232
Sum209.8
Variance0.0070889064
MonotonicityNot monotonic
2023-08-13T19:33:07.035779image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1.7 96
77.4%
1.6 8
 
6.5%
1.5 7
 
5.6%
1.9 5
 
4.0%
1.8 5
 
4.0%
1.4 2
 
1.6%
2 1
 
0.8%
ValueCountFrequency (%)
1.4 2
 
1.6%
1.5 7
 
5.6%
1.6 8
 
6.5%
1.7 96
77.4%
1.8 5
 
4.0%
1.9 5
 
4.0%
2 1
 
0.8%
ValueCountFrequency (%)
2 1
 
0.8%
1.9 5
 
4.0%
1.8 5
 
4.0%
1.7 96
77.4%
1.6 8
 
6.5%
1.5 7
 
5.6%
1.4 2
 
1.6%

base_attack_time
Real number (ℝ)

Distinct6
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.01613
Minimum90
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-13T19:33:07.243061image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile100
Q1100
median100
Q3100
95-th percentile119.25
Maximum125
Range35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.2767915
Coefficient of variation (CV)0.061527443
Kurtosis4.1428473
Mean102.01613
Median Absolute Deviation (MAD)0
Skewness1.9522316
Sum12650
Variance39.398112
MonotonicityNot monotonic
2023-08-13T19:33:07.433399image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
100 101
81.5%
110 8
 
6.5%
120 5
 
4.0%
115 4
 
3.2%
90 4
 
3.2%
125 2
 
1.6%
ValueCountFrequency (%)
90 4
 
3.2%
100 101
81.5%
110 8
 
6.5%
115 4
 
3.2%
120 5
 
4.0%
125 2
 
1.6%
ValueCountFrequency (%)
125 2
 
1.6%
120 5
 
4.0%
115 4
 
3.2%
110 8
 
6.5%
100 101
81.5%
90 4
 
3.2%

attack_point
Real number (ℝ)

Distinct22
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40322581
Minimum0.17
Maximum0.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-13T19:33:07.660027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile0.3
Q10.3
median0.4
Q30.5
95-th percentile0.5855
Maximum0.65
Range0.48
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.097325524
Coefficient of variation (CV)0.2413673
Kurtosis-0.57710839
Mean0.40322581
Median Absolute Deviation (MAD)0.1
Skewness0.35937712
Sum50
Variance0.0094722575
MonotonicityNot monotonic
2023-08-13T19:33:07.887027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.3 29
23.4%
0.4 20
16.1%
0.5 20
16.1%
0.35 14
11.3%
0.33 6
 
4.8%
0.46 5
 
4.0%
0.6 4
 
3.2%
0.45 4
 
3.2%
0.36 3
 
2.4%
0.56 3
 
2.4%
Other values (12) 16
12.9%
ValueCountFrequency (%)
0.17 1
 
0.8%
0.2 1
 
0.8%
0.25 1
 
0.8%
0.3 29
23.4%
0.33 6
 
4.8%
0.35 14
11.3%
0.36 3
 
2.4%
0.38 1
 
0.8%
0.39 1
 
0.8%
0.4 20
16.1%
ValueCountFrequency (%)
0.65 1
 
0.8%
0.6 4
 
3.2%
0.59 2
 
1.6%
0.56 3
 
2.4%
0.55 3
 
2.4%
0.53 1
 
0.8%
0.5 20
16.1%
0.467 1
 
0.8%
0.46 5
 
4.0%
0.45 4
 
3.2%

move_speed
Real number (ℝ)

Distinct12
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.60484
Minimum275
Maximum330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-13T19:33:08.098027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum275
5-th percentile280
Q1290
median300
Q3310
95-th percentile325
Maximum330
Range55
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.307777
Coefficient of variation (CV)0.047596631
Kurtosis-0.77183733
Mean300.60484
Median Absolute Deviation (MAD)10
Skewness0.37990468
Sum37275
Variance204.7125
MonotonicityNot monotonic
2023-08-13T19:33:08.284027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
290 23
18.5%
300 16
12.9%
285 14
11.3%
310 12
9.7%
295 12
9.7%
315 10
8.1%
305 10
8.1%
325 8
 
6.5%
280 6
 
4.8%
330 5
 
4.0%
Other values (2) 8
 
6.5%
ValueCountFrequency (%)
275 3
 
2.4%
280 6
 
4.8%
285 14
11.3%
290 23
18.5%
295 12
9.7%
300 16
12.9%
305 10
8.1%
310 12
9.7%
315 10
8.1%
320 5
 
4.0%
ValueCountFrequency (%)
330 5
 
4.0%
325 8
 
6.5%
320 5
 
4.0%
315 10
8.1%
310 12
9.7%
305 10
8.1%
300 16
12.9%
295 12
9.7%
290 23
18.5%
285 14
11.3%

legs
Categorical

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2
98 
0
14 
4
 
8
6
 
3
8
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row2
2nd row2
3rd row4
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 98
79.0%
0 14
 
11.3%
4 8
 
6.5%
6 3
 
2.4%
8 1
 
0.8%

Length

2023-08-13T19:33:08.506027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T19:33:08.755027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
2 98
79.0%
0 14
 
11.3%
4 8
 
6.5%
6 3
 
2.4%
8 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
2 98
79.0%
0 14
 
11.3%
4 8
 
6.5%
6 3
 
2.4%
8 1
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 98
79.0%
0 14
 
11.3%
4 8
 
6.5%
6 3
 
2.4%
8 1
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 124
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 98
79.0%
0 14
 
11.3%
4 8
 
6.5%
6 3
 
2.4%
8 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 98
79.0%
0 14
 
11.3%
4 8
 
6.5%
6 3
 
2.4%
8 1
 
0.8%

day_vision
Categorical

Distinct3
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1800
122 
800
 
1
1600
 
1

Length

Max length4
Median length4
Mean length3.9919355
Min length3

Characters and Unicode

Total characters495
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.6%

Sample

1st row1800
2nd row1800
3rd row1800
4th row1800
5th row1800

Common Values

ValueCountFrequency (%)
1800 122
98.4%
800 1
 
0.8%
1600 1
 
0.8%

Length

2023-08-13T19:33:08.979030image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T19:33:09.219027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1800 122
98.4%
800 1
 
0.8%
1600 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 248
50.1%
1 123
24.8%
8 123
24.8%
6 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 495
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 248
50.1%
1 123
24.8%
8 123
24.8%
6 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 248
50.1%
1 123
24.8%
8 123
24.8%
6 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 248
50.1%
1 123
24.8%
8 123
24.8%
6 1
 
0.2%

night_vision
Categorical

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
800
119 
1800
 
2
1200
 
1
1400
 
1
1000
 
1

Length

Max length4
Median length3
Mean length3.0403226
Min length3

Characters and Unicode

Total characters377
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)2.4%

Sample

1st row800
2nd row800
3rd row1200
4th row800
5th row800

Common Values

ValueCountFrequency (%)
800 119
96.0%
1800 2
 
1.6%
1200 1
 
0.8%
1400 1
 
0.8%
1000 1
 
0.8%

Length

2023-08-13T19:33:09.424027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T19:33:09.678159image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
800 119
96.0%
1800 2
 
1.6%
1200 1
 
0.8%
1400 1
 
0.8%
1000 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 249
66.0%
8 121
32.1%
1 5
 
1.3%
2 1
 
0.3%
4 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 377
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 249
66.0%
8 121
32.1%
1 5
 
1.3%
2 1
 
0.3%
4 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 377
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 249
66.0%
8 121
32.1%
1 5
 
1.3%
2 1
 
0.3%
4 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 249
66.0%
8 121
32.1%
1 5
 
1.3%
2 1
 
0.3%
4 1
 
0.3%

base_attack
Real number (ℝ)

Distinct49
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.754032
Minimum2
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-13T19:33:09.926156image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10.075
Q118.75
median28.25
Q333
95-th percentile42
Maximum66
Range64
Interquartile range (IQR)14.25

Descriptive statistics

Standard deviation10.60478
Coefficient of variation (CV)0.39638064
Kurtosis0.60466116
Mean26.754032
Median Absolute Deviation (MAD)6.5
Skewness0.080301543
Sum3317.5
Variance112.46137
MonotonicityNot monotonic
2023-08-13T19:33:10.202156image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
31 9
 
7.3%
32 7
 
5.6%
28 7
 
5.6%
37 7
 
5.6%
29 7
 
5.6%
25 6
 
4.8%
13 5
 
4.0%
34 4
 
3.2%
33 4
 
3.2%
14 4
 
3.2%
Other values (39) 64
51.6%
ValueCountFrequency (%)
2 1
 
0.8%
2.5 1
 
0.8%
6 1
 
0.8%
7 1
 
0.8%
8 1
 
0.8%
10 2
 
1.6%
10.5 2
 
1.6%
11 1
 
0.8%
12 2
 
1.6%
13 5
4.0%
ValueCountFrequency (%)
66 1
 
0.8%
48 1
 
0.8%
47 1
 
0.8%
44 1
 
0.8%
43.5 1
 
0.8%
43 1
 
0.8%
42 2
1.6%
40 3
2.4%
39 3
2.4%
38 1
 
0.8%

Interactions

2023-08-13T19:32:53.540699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:31:57.299968image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:00.974306image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:04.431217image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:07.833124image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:11.605487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:14.999166image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:18.470793image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:22.118437image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:25.341814image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:28.941031image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:32.577115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:35.956914image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:39.430141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:42.894141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:46.864329image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:50.165420image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:53.748699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:31:57.691705image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:01.178396image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:04.638419image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:08.053324image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:11.812487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:15.212166image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:18.676793image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:22.317435image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:25.561852image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:29.143031image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:32.782776image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:36.169089image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:39.643143image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:43.109141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:47.066330image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:50.372420image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:53.936699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:31:57.883706image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:01.352418image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:04.826018image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:08.248493image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:11.997487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:15.401166image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:18.861793image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:22.490435image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:25.760885image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:29.323072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:32.967926image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:36.358249image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:39.832141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:43.304141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:47.246330image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:50.555485image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:54.135700image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:31:58.085705image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:01.542420image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:05.016788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:08.453500image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:12.194556image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:15.602166image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:19.055413image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:22.675437image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:25.966885image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:29.514072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:33.162075image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:36.558416image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:40.032141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:43.508141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:47.435329image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:50.750485image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:54.351699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:31:58.308707image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:01.750418image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:05.233788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:08.672629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:12.410556image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:15.825166image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:19.539474image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:22.881435image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:26.193885image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:29.722072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:33.377796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:36.779099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:40.251141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:43.734141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:47.645388image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:50.964485image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:54.551678image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:31:58.513187image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:01.942420image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:05.432831image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:08.879629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:12.605208image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:16.026166image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:19.735474image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:23.070719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:26.404885image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:29.915072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:33.576797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:36.983099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:40.454141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:43.942141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:47.842386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:51.162485image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:54.758687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:31:58.727187image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:02.143420image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:05.640834image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:09.096639image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:12.811208image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:16.235847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:19.941477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:23.266719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:26.624888image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:30.116072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:33.783796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:37.198099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:40.666141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:44.160144image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:48.043389image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:51.369485image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:54.959687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:31:58.932187image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:02.337482image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:05.838831image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:09.306687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:13.008211image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:16.435848image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:20.136475image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:23.456719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:26.836885image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:30.309075image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:33.980796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:37.402099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:40.869141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:44.787205image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:48.237386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:51.567106image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:55.141687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:31:59.119187image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:02.512478image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:06.022831image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:09.496687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:13.191211image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:16.622847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:20.317515image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:23.624722image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:27.028887image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:30.821072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:34.161796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:37.590099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:41.055140image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:44.979271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:48.413386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:51.747182image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:55.363687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:31:59.343245image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:02.725012image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:06.240834image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:09.726687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:13.409601image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:16.844848image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:20.536515image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:23.833719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:27.255885image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:31.035072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:34.379836image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:37.812099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:41.278141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:45.209271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:48.626386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:51.968371image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:55.554721image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:31:59.538245image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:02.906015image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:06.430831image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:09.922687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:13.600605image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:17.039847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:20.724518image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:24.013719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:27.457885image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:31.217072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:34.570836image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:38.008099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:41.471141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:45.408271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:48.810389image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:52.157531image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:55.750721image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:31:59.739245image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:03.263012image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:06.624831image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:10.126687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:13.793604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:17.238849image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:20.919529image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:24.198719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:27.663885image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:31.406072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:34.758839image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:38.207099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:41.671141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:45.612271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:48.999386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:52.350648image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:55.954763image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:31:59.947304image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:03.459012image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:06.827867image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:10.337687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:13.995656image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:17.447847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:21.120529image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:24.391814image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:27.878885image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:31.604072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:34.959839image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:38.408099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:41.878141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:45.823271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:49.197386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:52.550682image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:56.160766image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:00.158306image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:03.660012image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:07.032868image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:10.766488image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:14.203656image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:17.658793image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:21.326529image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:24.586811image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:28.094885image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:31.803072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:35.165837image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:38.618099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:42.083141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:46.038271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:49.397420image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:52.755691image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:56.374902image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:00.375304image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:03.866262image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:07.246867image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:10.991487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:14.418107image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:17.874796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:21.537531image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:24.789811image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:28.320031image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:32.010072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:35.377836image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:38.834099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:42.299141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:46.254330image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:49.604420image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:52.966699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:56.562901image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:00.568303image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:04.049179image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:07.435124image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:11.188489image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:14.605107image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:18.067793image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:21.727434image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:24.966811image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:28.520031image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:32.192075image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:35.564692image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:39.026102image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:42.490141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:46.452329image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:49.782420image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:53.154699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:56.757904image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:00.769304image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:04.237182image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:07.632124image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:11.395487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:14.800108image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:18.266793image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:21.919435image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:25.150811image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:28.728031image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:32.381127image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:35.758745image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:39.225099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:42.689141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:46.655329image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:49.971421image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-13T19:32:53.341699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-08-13T19:33:10.506783image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
hero_idbase_health_regenbase_mana_regenbase_armorbase_strbase_agibase_intstr_gainagi_gainint_gainattack_rangeprojectile_speedattack_ratebase_attack_timeattack_pointmove_speedbase_attackprimary_attrattack_typebase_manalegsday_visionnight_vision
hero_id1.000-0.057-0.0290.0340.121-0.1360.1670.137-0.1100.042-0.0500.0210.167-0.024-0.1170.126-0.0890.0000.1220.0000.0000.0000.000
base_health_regen-0.0571.000-0.0650.1580.1510.090-0.3010.1840.073-0.315-0.482-0.3100.032-0.0640.0560.0980.2410.1750.3480.0660.0600.2820.237
base_mana_regen-0.029-0.0651.000-0.0600.012-0.0240.218-0.014-0.0410.2400.1550.0160.0470.0000.289-0.145-0.0900.0870.0680.0000.0000.0000.000
base_armor0.0340.158-0.0601.0000.072-0.188-0.0480.204-0.049-0.082-0.328-0.1850.179-0.0720.0100.2990.2380.0000.1810.6590.0000.0000.289
base_str0.1210.1510.0120.0721.000-0.293-0.1320.633-0.275-0.277-0.491-0.2570.265-0.1830.0560.1170.1440.3110.4790.0000.1050.3670.000
base_agi-0.1360.090-0.024-0.188-0.2931.0000.015-0.3700.713-0.0830.0370.032-0.2100.212-0.0330.067-0.1350.3170.0430.0000.0000.0000.000
base_int0.167-0.3010.218-0.048-0.1320.0151.000-0.243-0.1060.6280.4660.264-0.065-0.0520.010-0.047-0.2890.3650.4600.9750.0000.0000.000
str_gain0.1370.184-0.0140.2040.633-0.370-0.2431.000-0.253-0.336-0.515-0.2450.258-0.2920.1600.1340.2630.3730.4930.3710.0000.0000.000
agi_gain-0.1100.073-0.041-0.049-0.2750.713-0.106-0.2531.000-0.0710.0200.083-0.1360.1580.011-0.0290.0650.4010.1830.0000.0000.0000.000
int_gain0.042-0.3150.240-0.082-0.277-0.0830.628-0.336-0.0711.0000.6420.297-0.068-0.1790.056-0.220-0.0980.4600.5500.9670.0000.0000.000
attack_range-0.050-0.4820.155-0.328-0.4910.0370.466-0.5150.0200.6421.0000.547-0.175-0.019-0.050-0.347-0.3020.4010.9500.0000.0960.0000.000
projectile_speed0.021-0.3100.016-0.185-0.2570.0320.264-0.2450.0830.2970.5471.000-0.236-0.010-0.077-0.267-0.2440.1910.6010.0000.0000.0000.217
attack_rate0.1670.0320.0470.1790.265-0.210-0.0650.258-0.136-0.068-0.175-0.2361.000-0.1050.1140.0410.2800.1580.2810.0000.1070.0000.000
base_attack_time-0.024-0.0640.000-0.072-0.1830.212-0.052-0.2920.158-0.179-0.019-0.010-0.1051.0000.105-0.011-0.2100.0830.1430.0000.3300.0000.000
attack_point-0.1170.0560.2890.0100.056-0.0330.0100.1600.0110.056-0.050-0.0770.1140.1051.000-0.0230.2410.1090.1570.0000.0000.2510.346
move_speed0.1260.098-0.1450.2990.1170.067-0.0470.134-0.029-0.220-0.347-0.2670.041-0.011-0.0231.0000.0180.1800.3910.0000.1280.0000.000
base_attack-0.0890.241-0.0900.2380.144-0.135-0.2890.2630.065-0.098-0.302-0.2440.280-0.2100.2410.0181.0000.5390.3880.6590.1110.3610.202
primary_attr0.0000.1750.0870.0000.3110.3170.3650.3730.4010.4600.4010.1910.1580.0830.1090.1800.5391.0000.6710.0000.1450.0000.000
attack_type0.1220.3480.0680.1810.4790.0430.4600.4930.1830.5500.9500.6010.2810.1430.1570.3910.3880.6711.0000.0000.1830.0000.093
base_mana0.0000.0660.0000.6590.0000.0000.9750.3710.0000.9670.0000.0000.0000.0000.0000.0000.6590.0000.0001.0000.0000.0000.000
legs0.0000.0600.0000.0000.1050.0000.0000.0000.0000.0000.0960.0000.1070.3300.0000.1280.1110.1450.1830.0001.0000.0000.000
day_vision0.0000.2820.0000.0000.3670.0000.0000.0000.0000.0000.0000.0000.0000.0000.2510.0000.3610.0000.0000.0000.0001.0000.468
night_vision0.0000.2370.0000.2890.0000.0000.0000.0000.0000.0000.0000.2170.0000.0000.3460.0000.2020.0000.0930.0000.0000.4681.000

Missing values

2023-08-13T19:32:57.133902image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-13T19:32:57.943904image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

hero_idnameprimary_attrattack_typebase_healthbase_health_regenbase_manabase_mana_regenbase_armorbase_mrbase_strbase_agibase_intstr_gainagi_gainint_gainattack_rangeprojectile_speedattack_ratebase_attack_timeattack_pointmove_speedlegsday_visionnight_visionbase_attack
01Anti-MageagiMelee1200.75750.00.0251924121.62.81.815001.41000.3003102180080031.0
12AxestrMelee1202.50750.0-1.0252520182.82.01.61509001.71000.4003152180080032.0
23BaneallRanged1200.25750.01.0252323232.52.52.54009001.71000.3003054180012007.0
34BloodseekeragiMelee1200.25750.02.0252422172.73.12.01509001.71000.4303002180080038.0
45Crystal MaidenintRanged1200.25751.0-1.0251716162.21.63.36009001.71000.4502802180080033.0
56Drow RangeragiRanged1200.25750.00.0251620151.92.91.462512501.71000.5003002180080032.5
67EarthshakerstrMelee1201.00750.02.0252212183.71.42.115001.71000.4673152180080032.0
78JuggernautagiMelee1200.50750.00.0252034142.22.81.415001.41100.3303002180080021.0
89MiranaallRanged1200.25750.4-2.0252024221.72.51.26309001.71100.350290218008008.0
910MorphlingagiRanged1200.50750.5-2.0252324193.03.91.835013001.51000.5002850180080013.5
hero_idnameprimary_attrattack_typebase_healthbase_health_regenbase_manabase_mana_regenbase_armorbase_mrbase_strbase_agibase_intstr_gainagi_gainint_gainattack_rangeprojectile_speedattack_ratebase_attack_timeattack_pointmove_speedlegsday_visionnight_visionbase_attack
114120PangolierallMelee1200.25750.02.0251718162.52.51.91509001.71000.333002180080021.0
115121GrimstrokeintRanged1200.25750.00.0252118252.41.93.86009001.71000.352900180080023.0
116123HoodwinkagiRanged1200.25750.00.0251724212.03.62.957518002.01000.403104180080025.5
117126Void SpiritallMelee1200.25750.6-1.0252219242.62.23.12009001.71000.352952180080017.0
118128SnapfireallRanged1200.75750.01.0252016213.21.22.150018001.61000.353002180080011.0
119129MarsstrMelee1200.25750.5-1.0252320213.71.72.22509001.81000.403102180080030.0
120135DawnbreakerstrMelee1200.25750.01.0252714203.41.72.21509001.71000.463102180080031.0
121136MarciallMelee1200.25750.00.0252320193.02.01.515001.71000.303152180080018.0
122137Primal BeaststrMelee1200.25750.02.0252615164.31.01.41509001.81000.603102180080037.0
123138MuertaintRanged1200.25750.0-1.0251922242.03.03.357530001.61000.352952180080028.0